Radio and PodcastLive Radio & PodcastsOpening Radio and Podcast...
Radio and PodcastLive Radio & PodcastsFetching podcast shows and categories...
Radio and PodcastLive Radio & PodcastsFetching podcast episodes...
Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our v...
This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a spac...
This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed...
In this episode of Learning Machines 101, we review Chapter 6 of my book "Statistical Machine Learning" which introduces methods for analyzi...
This particular podcast covers the material from Chapter 5 of my new book "Statistical Machine Learning: A unified framework" which is now a...
The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled "Statistical Machine Learning:...
This particular podcast covers the material in Chapter 3 of my new book "Statistical Machine Learning: A unified framework" with expected pu...
This particular podcast covers the material in Chapter 2 of my new book "Statistical Machine Learning: A unified framework" with expected pu...
This particular podcast covers the material in Chapter 1 of my new (unpublished) book "Statistical Machine Learning: A unified framework". I...
This particular podcast (Episode 78 of Learning Machines 101) is the initial episode in a new special series of episodes designed to provide...
In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (B...
In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Informat...
In this episode, we explore the question of what can computers do as well as what computers can't do using the Turing Machine argument. Spec...
In this episode we will learn how to use "rules" to represent knowledge. We discuss how this works in practice and we explain how these idea...
This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that A...
This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction...
In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense int...
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering tech...
This 69 th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a fo...
This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithm...
In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer t...
In this episode of Learning Machines 101 ( www.learningmachines101.com ) we discuss how to solve constraint satisfaction inference problems...
In this episode rerun we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of...
In this rerun of episode 24 we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves i...
This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but d...
This 62nd episode of Learning Machines 101 ( www.learningmachines101.com ) discusses how to design reinforcement learning machines using you...
This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden's recent visit to the 2015 Neu...
This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to m...
I discuss the concept of a "neural network" by providing some examples of recent successes in neural network machine learning algorithms and...
In this 58th episode of Learning Machines 101, I'll be discussing an important new scientific breakthrough published just last week for the...
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money...
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We expla...
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning mach...
Welcome to the 54th Episode of Learning Machines 101 titled "How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms...
In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization...
Today, we discuss a simple yet powerful idea which began popular in the machine learning literature in the 1990s which is called "The Kernel...
This particular podcast is a RERUN of Episode 20 and describes step by step how to download free software which can be used to make predicti...
In this episode we will explain how to download and use free machine learning software from the website: www.learningmachines101.com . This...
In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learnin...
In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning m...
We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as iden...
In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student l...
In this episode we discuss just one out of the 102 different posters which was presented on the first night of the 2015 Neural Information P...
This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neu...
Welcome to the 43rd Episode of Learning Machines 101! We are currently presenting a subsequence of episodes covering the events of the recen...
This is the second of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Ne...
This is the first of a short subsequence of podcasts which provides a summary of events associated with Dr. Golden’s recent visit to the 201...
In this episode we introduce a very powerful approach for computing semantic similarity between documents. Here, the terminology “document”...
In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered colle...
In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge...
In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Supp...